You can tell right away, from its title, that this paper is going to be a must-read for empirical economists. And note the words, "Small-sample" in the title - that sounds interesting.

Here's a compilation of Beth's six tweets:

"Econ friends, @jepusto and I have a new paper out that we would love to share. It’s about clustering your standard errors (more below).

‏Any suggestions for how to get these methods out to economists given that we aren’t NBER?

Summary: Our paper provides small-sample adjustments to cluster robust variance estimation (CRVE). It can be used with panel data, experimental data, and regression. You can implement the method in a Stata macro called REG_SANDWICH and an R package called clubSandwich.

Why do you need this? Regular CRVE doesn’t do so well, even with as many as 100 clusters (!). In fact, CRVE only gives you appropriate Type I error when your covariates are balanced.

What did we do? We extended the bias-robust linearization method (BRL) by Bell & McCaffrey in three ways: (1) in addition to a t-test, there is now an F-test; (2) We can handle the inclusion of fixed effects; (3) You get the same results whether you use FE or absorption.

How does it work? The adjustment inflates the standard errors a small bit. But more importantly, it provides Satterthwaite-type degrees of freedom that are more appropriate. The result is a test we call the ‘Approximate Hotelling’s T-squared’ (AHT) test.

We’d love to share the work broadly, so if you have ideas, please let us know. Thanks!"